It is not uncommon for signals to undergo random, spurious distortion during transmission. Such problems are inherently difficult to detect and solve using traditional methods at least because of the random and ephemeral nature of the distortion and a finite amount of capture memory available in present day analyzer hardware. Current methodology used to troubleshoot distortion problems usually includes capturing large, disjoint sets of sample data and analyzing each one for clues. This approach can be crude, inefficient, and often technically unfeasible due to unreasonable hardware requirements, for example, large amounts of memory and processing power. One practical exemplary application is cognitive radio. Cognitive radio is a term used to describe intelligent wireless communications devices built on top of software defined radio platforms. The purpose of such a device is to provide both reliable communication links and efficient utilization of valuable radio spectrum resources. An ideal system of this type should be capable of learning from and adapting to input stimuli received from the environment.
A detector or method that is capable of analyzing signal waveforms in real-time for anomalous features would be desirable.